Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints

• URL: http://arxiv.org/abs/2508.13663v1
• Date: Tue, 19 Aug 2025 09:09:07 GMT
• Title: Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
• Authors: Daniel Daza, Alberto Bernardi, Luca Costabello, Christophe Gueret, Masoud Mansoury, Michael Cochez, Martijn Schut,
• Abstract summary: We propose a Neural Query Reranker (NQR) designed to adjust query answer scores by incorporating soft constraints without disrupting the original answers to a query.<n>NQR operates interactively, refining answers based on incremental examples of preferred and non-preferred entities.<n>Our experiments demonstrate that NQR can capture soft constraints while maintaining robust query answering performance.
• Score: 10.209426700875866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we introduce the problem of query answering with soft constraints. We propose a Neural Query Reranker (NQR) designed to adjust query answer scores by incorporating soft constraints without disrupting the original answers to a query. NQR operates interactively, refining answers based on incremental examples of preferred and non-preferred entities. We extend existing QA benchmarks by generating datasets with soft constraints. Our experiments demonstrate that NQR can capture soft constraints while maintaining robust query answering performance.

Related papers

• Query Suggestion for Retrieval-Augmented Generation via Dynamic In-Context Learning [42.850404278428954]
  We introduce robust dynamic few-shot learning which retrieves examples from relevant user queries.<n>We show that our system can be self-learned, for instance on prior user queries, and is therefore easily applicable in practice.
  arXiv  Detail & Related papers  (2026-01-13T00:56:38Z)
• Tractable Responsibility Measures for Ontology-Mediated Query Answering [3.1952340441132474]
  We study the complexity of computing responsibility scores in the setting of ontology-mediated query answering.<n>We show that such measures enjoy minimal data complexity for classes of first-order-rewritable queries.<n>We prove that intractability applies already to atomic queries if the language supports conjunction.
  arXiv  Detail & Related papers  (2025-07-31T02:08:12Z)
• The benefits of query-based KGQA systems for complex and temporal questions in LLM era [55.20230501807337]
  Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions.<n> Query-based knowledge graph QA (KGQA) offers a modular alternative by generating executable queries instead of direct answers.<n>We explore multi-stage query-based framework for WikiData QA, proposing multi-stage approach that enhances performance on challenging multi-hop and temporal benchmarks.
  arXiv  Detail & Related papers  (2025-07-16T06:41:03Z)
• Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering [48.43453390717167]
  We present and tackle the problem of Embodied Question Answering with Situational Queries (S-EQA) in a household environment.<n>Unlike prior EQA work, situational queries require the agent to correctly identify multiple object-states and reach a consensus on their states for an answer.<n>We introduce a novel Prompt-Generate-Evaluate scheme that wraps around an LLM's output to generate unique situational queries and corresponding consensus object information.
  arXiv  Detail & Related papers  (2024-05-08T00:45:20Z)
• Open-Set Knowledge-Based Visual Question Answering with Inference Paths [79.55742631375063]
  The purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases. We propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity) Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process.
  arXiv  Detail & Related papers  (2023-10-12T09:12:50Z)
• Answering Ambiguous Questions via Iterative Prompting [84.3426020642704]
  In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. One approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. We present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions.
  arXiv  Detail & Related papers  (2023-07-08T04:32:17Z)
• Logical Message Passing Networks with One-hop Inference on Atomic Formulas [57.47174363091452]
  We propose a framework for complex query answering that decomposes the Knowledge Graph embeddings from neural set operators. On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning. Our approach yields the new state-of-the-art neural CQA model.
  arXiv  Detail & Related papers  (2023-01-21T02:34:06Z)
• DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases [81.19499764899359]
  We propose a novel framework DecAF that jointly generates both logical forms and direct answers. DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks.
  arXiv  Detail & Related papers  (2022-09-30T19:51:52Z)
• Query2Particles: Knowledge Graph Reasoning with Particle Embeddings [49.64006979045662]
  We propose a query embedding method to answer complex logical queries on knowledge graphs with missing edges. The answer entities are selected according to the similarities between the entity embeddings and the query embedding. A complex KG query answering method, Q2P, is proposed to retrieve diverse answers from different areas over the embedding space.
  arXiv  Detail & Related papers  (2022-04-27T11:16:08Z)
• Query Embedding on Hyper-relational Knowledge Graphs [0.4779196219827507]
  Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs. We extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries.
  arXiv  Detail & Related papers  (2021-06-15T14:08:50Z)
• Answering Counting Queries over DL-Lite Ontologies [0.0]
  We introduce a general form of counting query, relate it to previous proposals, and study the complexity of answering such queries. We consider some practically relevant restrictions, for which we establish improved complexity bounds.
  arXiv  Detail & Related papers  (2020-09-02T11:10:21Z)
• Bounded-Memory Criteria for Streams with Application Time [0.0]
  Bounded-memory computability continues to be in the focus of those areas of AI and databases that deal with feasible computations over streams. This work presents criteria for bounded-memory computability of select-project-join (SPJ) queries over streams with application time.
  arXiv  Detail & Related papers  (2020-07-30T12:05:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.